Time series decomposition chart

Microsoft Corporation

Understand the time series components using “Seasonal and Trend decomposition using Loess”

It is a common scenario: A practitioner has sales data for the past several months and wants to make sense of the time series data. Typically next step would be to perform a forecast for the next time period. The decomposition of time series is a statistical method that splits a time series into several components, each representing one of the underlying processes. There are three components that are typically of interest: trend, seasonality and noise. Time series decomposition is an essential analytics tool to understand time series components and to improve a forecast. You can control the algorithm parameters and the visual attributes to suit your needs. The current visual implements the well-known “Seasonal and Trend decomposition using Loess” approach.

Highlighted features:

The underlying algorithm requires the input data to be equally spaced time series

Seasonal factor can be found automatically or set by user

The choice of additive or multiplicative model can be performed automatically or set by user

Time series decomposition is a powerful analytics tool. Seven different modes of time series visualization are provided to allow the analyst to drill down into different aspects of data